Instructions to use itsnotacreativeuser/SmolVLM-256M-ScreenTask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use itsnotacreativeuser/SmolVLM-256M-ScreenTask with PEFT:
```
Task type is invalid.
```

How to use itsnotacreativeuser/SmolVLM-256M-ScreenTask with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="itsnotacreativeuser/SmolVLM-256M-ScreenTask")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("itsnotacreativeuser/SmolVLM-256M-ScreenTask", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use itsnotacreativeuser/SmolVLM-256M-ScreenTask with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itsnotacreativeuser/SmolVLM-256M-ScreenTask"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itsnotacreativeuser/SmolVLM-256M-ScreenTask",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/itsnotacreativeuser/SmolVLM-256M-ScreenTask

SGLang

How to use itsnotacreativeuser/SmolVLM-256M-ScreenTask with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "itsnotacreativeuser/SmolVLM-256M-ScreenTask" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itsnotacreativeuser/SmolVLM-256M-ScreenTask",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "itsnotacreativeuser/SmolVLM-256M-ScreenTask" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itsnotacreativeuser/SmolVLM-256M-ScreenTask",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use itsnotacreativeuser/SmolVLM-256M-ScreenTask with Docker Model Runner:
```
docker model run hf.co/itsnotacreativeuser/SmolVLM-256M-ScreenTask
```

Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string

SmolVLM-256M-ScreenTask

This model is a fine-tuned version of HuggingFaceTB/SmolVLM-256M-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.8402

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 0.35
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.857	0.0599	20	2.7762
1.8633	0.1199	40	1.8233
1.1164	0.1798	60	1.0640
0.8947	0.2397	80	0.8909
0.8471	0.2996	100	0.8402

Framework versions

PEFT 0.18.0
Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

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Model tree for itsnotacreativeuser/SmolVLM-256M-ScreenTask

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Quantized

HuggingFaceTB/SmolVLM-256M-Instruct

Adapter

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this model

itsnotacreativeuser
/

SmolVLM-256M-ScreenTask

SmolVLM-256M-ScreenTask

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for itsnotacreativeuser/SmolVLM-256M-ScreenTask

Dataset used to train itsnotacreativeuser/SmolVLM-256M-ScreenTask